Merge pull request #1610 from vaerksted/master

fix typos

NEWS.md

@@ -253,7 +253,7 @@ Each release typically has a number of minor bug fixes beyond what is listed her
   * Add a `group` aesthetic to allow plotting lines of the same color without
     using multiple layers.
 
-  * Better herustics for choosing histogram bin counts.
+  * Better heuristics for choosing histogram bin counts.
 
   * Add `Guide.manual_color_key` which lets one completely specify the colors
     and entries in a color key.

docs/src/index.md

@@ -58,7 +58,7 @@ first plot is to rarely close your REPL session.
 establishes a mechanism which automatically reloads code after it has been
 modified, thereby reducing the need to restart.
 
-Alternatively, one can avoid the first-time-to-plot penalty altogther by
+Alternatively, one can avoid the first-time-to-plot penalty altogether by
 ahead-of-time (AOT) compiling Gadfly into the Julia system image using
 [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl).
 
@@ -69,7 +69,7 @@ mkdir $HOME/JuliaGadflySysImage
 cd $HOME/JuliaGadflySysImage
 ```
 
-one can complile Gadfly.jl and create the sysimage as follows:
+one can compile Gadfly.jl and create the sysimage as follows:
 
 ```julia
 (@v.1.4) pkg> add PackageCompiler
@@ -80,7 +80,7 @@ julia> create_sysimage(:Gadfly; sysimage_path="GadFlySysimage.so")
 julia> exit()
 ```
 
-At the end of the resulting copius output will be the command to launch this
+At the end of the resulting copious output will be the command to launch this
 custom version of julia: something like `julia --sysimage $HOME/JuliaGadflySysImage/GadFlySysimage.so`.
 Make it convenient by putting an alias in your .bashrc: `alias julia-gadfly="julia --sysimage ..."`.
 

docs/src/man/backends.md

@@ -26,7 +26,7 @@ p = plot(...)
 draw(SVG("foo.svg", 6inch, 4inch), p)
 ```
 
-one can more succintly use Julia's function chaining syntax:
+one can more succinctly use Julia's function chaining syntax:
 
 ```julia
 p |> SVG("foo.svg", 6inch, 4inch)

docs/src/man/compositing.md

@@ -107,7 +107,7 @@ title(hstack(p1,p2), "My creative title")
 
 ## Layers
 
-_Introduction:_ Draw multiple layers onto the same plot by inputing `Layer` objects to `plot`.
+_Introduction:_ Draw multiple layers onto the same plot by inputting `Layer` objects to `plot`.
 
 ```@setup layer
 using Gadfly, RDatasets, Distributions

src/Gadfly.jl

@@ -34,7 +34,7 @@ export Plot, Layer, Theme, Col, Row, Scale, Coord, Geom, Guide, Stat, Shape, ren
 @deprecate star1 Shape.star1
 @deprecate star2 Shape.star2
 @deprecate hexagon Shape.hexagon
-@deprecate octogon Shape.octogon
+@deprecate octagon Shape.octagon
 @deprecate hline Shape.hline
 @deprecate vline Shape.vline
 
@@ -112,7 +112,7 @@ const ElementOrFunction{T <: Element} = Union{Element, Base.Callable, Theme}
 const gadflyjs = joinpath(dirname(Base.source_path()), "gadfly.js")
 
 
-# Set prefereed canvas size when rendering a plot without an explicit call to
+# Set preferred canvas size when rendering a plot without an explicit call to
 # `draw`.
 """
     set_default_plot_size(width::Compose.MeasureOrNumber,

src/bincount.jl

@@ -8,12 +8,12 @@
 # methods that all seem to give good results with little difference. Birge's
 # method is simple (it's just AIC with an extra logarithmic term), has a decent
 # theoretical justification, and is general enough to apply to multidimensional
-# and non-regular bin selecetion problems. Though, the penalty they use was
+# and non-regular bin selection problems. Though, the penalty they use was
 # optimized for regular histograms, so may need to be tweaked.
 #
 # The Birge penalty is
 #    penalty(D) = D - 1 + log(D)^2.5
-# where D is the number of bins. The 2.5 constant was arrived at emperically by
+# where D is the number of bins. The 2.5 constant was arrived at empirically by
 # optimizing over samples from example density functions.
 #
 
@@ -89,7 +89,7 @@ end
 #
 # Returns:
 #   A tuple of the form (d, bincounts, x_max), where d gives the optimal number of
-#   bins, and bincounts is an array giving the number of occurances in each
+#   bins, and bincounts is an array giving the number of occurrences in each
 #   bin, and x_max is the end point of the final bin.
 #
 function choose_bin_count_1d(xs::AbstractVector, d_min=1, d_max=150)
@@ -147,7 +147,7 @@ end
 #
 # Returns
 #   A tuple of the form (d, bincounts, x_max), where d gives the optimal number of
-#   bins, and bincounts is an array giving the number of occurances in each
+#   bins, and bincounts is an array giving the number of occurrences in each
 #   bin, and x_max is the end point of the final bin.
 #
 function choose_bin_count_1d_discrete(xs::AbstractArray, xs_set::AbstractArray,
@@ -222,7 +222,7 @@ function choose_bin_count_2d(xs::AbstractVector, ys::AbstractVector,
                              xminbincount::Int, xmaxbincount::Int,
                              yminbincount::Int, ymaxbincount::Int)
 
-    # For two demensions, I'm just going to optimize the marginal bin counts.
+    # For two dimensions, I'm just going to optimize the marginal bin counts.
     # This might not be optimal, but its simple and fast.
 
     x_min, x_max = Gadfly.concrete_minimum(xs), Gadfly.concrete_maximum(xs)

src/geom/hvband.jl

@@ -1,7 +1,7 @@
 """
     Geom.band[(; orientation=:vertical)]
 
-Draw bands across the plot canvas with a horizontal span specifed by `xmin` and `xmax` if `orientation` is `:vertical`, or a vertical span specified by `ymin` and `ymax` if the `orientation` is `:horizontal`.
+Draw bands across the plot canvas with a horizontal span specified by `xmin` and `xmax` if `orientation` is `:vertical`, or a vertical span specified by `ymin` and `ymax` if the `orientation` is `:horizontal`.
 
 This geometry is equivalent to [`Geom.rect`](@ref) with [`Stat.band`](@ref).
 """

src/geom/label.jl

@@ -100,7 +100,7 @@ function deferred_label_context(geom::LabelGeometry,
     # once, rather than every iteration of annealing.
     possible_overlaps = [Array{Int}(undef, 0) for _ in 1:length(label_point_boxes)]
 
-    # TODO: this whole thing would be much more effecient if we forbid from
+    # TODO: this whole thing would be much more efficient if we forbid from
     # the start labels that overlap points. We should be able to precompute
     # that, since they're static.
 
@@ -129,7 +129,7 @@ function deferred_label_context(geom::LabelGeometry,
             0mm < a.x0[2] && a.x0[2] + a.a[2] < parent_box.a[2]
 
     # This variable holds the value of the objective function we wish to
-    # minimize. A label overlap is a penalty of 1. Other penaties (out of bounds
+    # minimize. A label overlap is a penalty of 1. Other penalties (out of bounds
     # labels, hidden labels) or calibrated to that.
     total_penalty = 0.0
 

src/guide.jl

@@ -122,7 +122,7 @@ end
 
 
 # A guide graphic is a position associated with one or more contexts.
-# Multiple contexts represent multiple layout possibilites that will be
+# Multiple contexts represent multiple layout possibilities that will be
 # optimized over.
 struct PositionedGuide
     ctxs::Vector{Context}

src/mapping.jl

@@ -31,7 +31,7 @@ end # module Col
 
 module Row
 
-# represent a row index correspondig to a set of columns
+# represent a row index corresponding to a set of columns
 struct GroupedColumnRowIndex
     columns::Union{Nothing,Vector}
 end
@@ -91,7 +91,7 @@ function meltdata(U::AbstractVector, colgroups_::Vector{Col.GroupedColumn})
         end
 
         # otherwise it doesn't make much sense
-        error("Col.index/Col.value can only be used without arguments when plotting an array of heterogenous arrays")
+        error("Col.index/Col.value can only be used without arguments when plotting an array of heterogeneous arrays")
     end
     colgroup = first(colgroups)
     colmap = Dict{Any, Int}()
@@ -208,7 +208,7 @@ function _evalmapping!(mapping::Dict, data_source, data::Data)
     return data_source
 end
 
-# Evalute aesthetic mappings producting a Data instance.
+# Evaluate aesthetic mappings producing a Data instance.
 evalmapping!(mapping::Dict, data_source::MeltedData, data::Data) =
     _evalmapping!(mapping, data_source, data)
 

src/scale.jl

@@ -328,7 +328,7 @@ easiest way to get that data to plot appropriately.
 `labels` is either a function which maps a coordinate value to a string label,
 or a vector of strings of the same length as the number of unique values in the
 aesthetic.  `levels` gives values for the scale.  Order will be respected and
-anything in the data that's not respresented in `levels` will be set to
+anything in the data that's not represented in `levels` will be set to
 `missing`.  `order` is a vector of integers giving a permutation of the levels
 default order.
 

src/scale/scales.jl

@@ -138,7 +138,7 @@ function apply_scale(scale::ContinuousSizeScale, aess::Vector{Gadfly.Aesthetics}
     Δ = scale.trans.f(ticks[end])-scale.trans.f(ticks[1])
     labels = scale.trans.label(ticks)
 
-# Transform ticks e.g. to areas/porportions/sizes
+# Transform ticks e.g. to areas/proportions/sizes
     keyvals = if scale.maxvalue===nothing
             showvals = false
             scale.trans.f.(ticks.-smin)

src/theme.jl

@@ -273,7 +273,7 @@ current_theme() = theme_stack[end]
 """
     style(; kwargs...) -> Theme
 
-Return a new `Theme` that is a copy of the current theme as modifed by the
+Return a new `Theme` that is a copy of the current theme as modified by the
 attributes in `kwargs`.  See [Themes](@ref) for available fields.
 
 # Examples

src/ticks.jl

@@ -39,8 +39,8 @@ optimize_ticks() = Any[]
 # tight fit around the data, optimal number of ticks, and simple numbers.
 #
 # Args:
-#   x_min: minimum value occuring in the data.
-#   x_max: maximum value occuring in the data.
+#   x_min: minimum value occurring in the data.
+#   x_max: maximum value occurring in the data.
 #   Q: tick intervals and scores
 #   k_min: minimum number of ticks
 #   k_max: maximum number of ticks

test/testscripts/stat_binmean.jl

@@ -9,7 +9,7 @@ p2 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth", color =
 p3 = plot(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth", color="Species", Stat.binmean(n=3), Geom.point)
 # integer works
 p4 = plot(x= rand(1:10, 100), y = rand(1:10, 100), Stat.binmean, Geom.point)
-#  surimpose regression
+#  superimpose regression
 p5 = plot(
 		layer(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth", color="Species", Stat.binmean(n=5), Geom.point),
 		layer(dataset("datasets", "iris"), x="SepalLength", y="SepalWidth", color="Species", Geom.smooth(method=:lm))